The present invention relates to methods and devices used to detect irregularities in the beating of an animal heart, generally known as xe2x80x9carrhythmias.xe2x80x9d More particularly, the invention relates to a method and an apparatus to detect atrial fibrillation (xe2x80x9cAFxe2x80x9d).
As is well known in the medical field, the contraction of an animal heart is controlled by a series of electrical signals that originate in the sinus node of the right atrium. These signals can be recorded and the record can be used as a diagnostic and treatment tool. An electrocardiogram (xe2x80x9cECGxe2x80x9d) is a graphic display of the electrical signals that cause the heart to contract. A representative ECG waveform 10 is shown in FIG. 1. The ECG 10 includes a number of crests (generally referred to as xe2x80x9cwavesxe2x80x9d) and a number of troughs. A normal ECG, such as the ECG waveform 10, includes a P wave, which represents the electrical potential generated as atrial cells in the heart depolarize before contraction. Following the P wave is a crest and trough combination known as the QRS complex, which includes Q, R, and S waves. The QRS complex is caused by the electrical potential generated when the ventricular muscle cells depolarize before contraction. Following the QRS complex is a T wave. The T wave is caused by electrical potential generated as the ventricles of the heart recover from the state of the polarization. Following the T wave is a short period of relative inactivity. A new contraction begins with a second P wave, P2.
A variety of methods and devices have been developed to assist physicians in interpreting ECGs. One such tool is known as EKpro detection software, which is available from GE Medical Systems Information Technologies, Inc., the assignee of the present application. EKpro software is designed for monitoring ECG signals in adults and paced patients. EKpro software has particular application in detecting atrial fibrillation. AF is identified by an irregular heart rhythm and is clinically defined as uncoordinated contractions of the atria. The ECG of a patient suffering from atrial fibrillation typically demonstrates irregular ventricular contractions and the absence of P waves. If allowed to continue, AF can cause decreases in exercise tolerance and left ventricular function. In more severe cases, AF can lead to a fatal medical condition.
The problems associated with AF can be reversed if sinus rhythm can be restored. The identification of AF allows a caregiver to administer a treatment to control symptoms and to prevent more serious complications. Most often, the treatment is specific to the nature of the atrial fibrillation suffered by the patient and, in particular, the heart rate of the patient, the symptoms suffered by the patient, and the duration of the AF events. Some current software systems attempt to detect AF based on ventricular activity. However, these types of systems can, in general, only suggest that an irregular rhythm may be caused by atrial fibrillation. One version of the EKpro product mentioned above is used in Holter monitoring, and determines the probability of an AF condition using a Hidden Markov Model (xe2x80x9cHMMxe2x80x9d) methodology. While the EKpro product is better than many others in predicting an AF condition, it still suffers from the problem of relying solely on ventricular data, and is dependent upon the data used to train it. In order to provide accurate predictions a complete data set covering all irregular heart rhythms, not just AF, is required. Since such data sets are often difficult to compile, a system solely dependent on a HMM methodology can be biased.
Another difficulty with present systems is that many are not, in general, able to specifically identify AF over other irregular rhythms. The inability to specifically identify the exact irregular rhythm suffered by the patient is problematic in hospital environments, where most patient monitoring is alarm-based. That is, whenever any irregular condition is detected, an alarm is set off. When an alarm sounds, a medical professional must respond. The medical professional can assess the situation and identify if the alarm is true or false. If there are too many false alarms, the medical professional may become desensitized, responding slowly or lackadaisically to alarms. This may cause a professional to respond inadequately when a severe or critical condition occurs.
Accordingly, it would be desirable to have an improved method and device to detect AF. In addition, since atrial fibrillation is typically not a life-threatening arrhythmia, an alternative solution to alarm-based monitoring system would be desirable so that medical professionals could respond in a manner that is proportionate to the significance of the irregular event, rather than treating all events equally.
The invention provides a method and apparatus that detects AF based upon a ventricular activity analysis, P wave activity, similarities in R wave to R wave intervals, and a state evaluation. In one embodiment, the invention includes a beat classification module that receives ECG information as an input. The beat classification module determines whether the heart beat being analyzed falls within classifications that are suitable for use in analyzing whether an AF condition exists. If the beat falls within a class suitable for analysis, the ECG information is fed to an interval calculator. The interval calculator determines the time interval between successive R waves (the xe2x80x9cRR intervalxe2x80x9d). The information from the interval calculator is provided to a probability engine and to a contextual analysis module. The probability engine is designed to detect atrial fibrillation based upon beat classification and RR interval values from the interval calculator. The probability engine outputs a state variable that indicates whether an AF condition is present. The contextual analysis module matches predefined maps to the running map of the current ECG information. The contextual analysis module also determines the similarity between consecutive RR intervals. In addition, the classifications determined by the beat classification module are used by the contextual analysis module to check for sequences of matching classes. This analysis imposes a refractory period before the triggering of an alarm, in those circumstances where an AF condition exists.
ECG information is also supplied to a P-wave detection module. The P-wave detection module receives beat classification information from the beat classification module. In one embodiment of the invention, each beat shape is represented by a template, with the dominant shape of the ECG information being analyzed represented in a xe2x80x9cfavoritexe2x80x9d template. When normal dominant beats are detected, they are used to incrementally update the favorite template. The favorite template is then used to determine whether P-waves are present in the dominant beat class. Information from the probability analysis engine, the contextual analysis module, and the P-wave detection module is then provided to a state evaluation module. The state evaluation module uses the outputs of the three noted modules to determine whether an atrial fibrillation condition exists. If the probability engine module outputs a state variable indicating an atrial fibrillation condition and the P-wave detection and contextual analysis modules output negative indicators, then an atrial fibrillation condition output is generated. However, before the state evaluation module outputs an alarm, the atrial fibrillation condition must exist for a specified amount of time. Likewise, a non-atrial fibrillation condition must exist for specified amount of time before an alarm is disabled.
The invention may also be embodied in a method. The method includes classifying ECG information, determining an interval between recurring events in the ECG information, determining the probability that an irregular condition exists based on classifying the ECG information and determining an interval between recurring events, and generating a state variable based on the determined probability. The state variable provides an indication of whether an irregular condition exists. In the invention, this indication is supplemented by generating a contextual output based on matching predefined maps to a current map of the ECG information, determining the presence of a P wave in the ECG information, and generating a detection output based on the presence of a P wave. The ultimate determination of whether the irregular condition exists is based on the state variable, the contextual output, and the detection output.